Analysis of Time Series
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This document discusses time series analysis. It defines a time series as values of a variable ordered over time. Examples of time series include climate data, financial data, and demographic data. Time series analysis is important for understanding past behavior, predicting the future, evaluating programs, and facilitating comparisons. Components of a time series include trends, cyclic variations, seasonal variations, and irregular variations. Several methods are discussed for measuring and decomposing these components, including moving averages, least squares, and seasonal indices.
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UNIVARIATE & BIVARIATE ANALYSIS
UNIVARIATE BIVARIATE & MULTIVARIATE
UNIVARIATE ANALYSIS
-One variable analysed at a time
BIVARIATE ANALYSIS
-Two variable analysed at a time
MULTIVARIATE ANALYSIS
-More than two variables analysed at a time
TYPES OF ANALYSIS
DESCRIPTIVE ANALYSIS
INFERENTIAL ANALYSIS
DESCRIPTIVE ANALYSIS
Transformation of raw data
Facilitate easy understanding and interpretation
Deals with summary measures relating to sample data
Eg-what is the average age of the sample?
INFERENTIAL ANALYSIS
Carried out after descriptive analysis
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1. Prepare frequency distribution of each variable
Missing Data
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3 measures of central tendency
1.Mean
2.Median
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x
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Computed for ratio, interval or ordinal scale.
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This document discusses time series analysis and its components. It covers:
- The components of a time series include trends, seasonal variations, cyclical movements, and irregular fluctuations.
- Time series can be analyzed using either an additive or multiplicative model depending on the independence of the components.
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UNIVARIATE & BIVARIATE ANALYSIS
UNIVARIATE BIVARIATE & MULTIVARIATE
UNIVARIATE ANALYSIS
-One variable analysed at a time
BIVARIATE ANALYSIS
-Two variable analysed at a time
MULTIVARIATE ANALYSIS
-More than two variables analysed at a time
TYPES OF ANALYSIS
DESCRIPTIVE ANALYSIS
INFERENTIAL ANALYSIS
DESCRIPTIVE ANALYSIS
Transformation of raw data
Facilitate easy understanding and interpretation
Deals with summary measures relating to sample data
Eg-what is the average age of the sample?
INFERENTIAL ANALYSIS
Carried out after descriptive analysis
Inferences drawn on population parameters based on sample results
Generalizes results to the population based on sample results
Eg-is the average age of population different from 35?
DESCRIPTIVE ANALYSIS OF UNIVARIATE DATA
1. Prepare frequency distribution of each variable
Missing Data
Situation where certain questions are left unanswered
Analysis of multiple responses
Measures of central tendency
3 measures of central tendency
1.Mean
2.Median
3.Mode
MEAN
Arithmetic average of a variable
Appropriate for interval and ratio scale data
x
MEDIAN
Calculates the middle value of the data
Computed for ratio, interval or ordinal scale.
Data needs to be arranged in ascending or descending order
MODE
Point of maximum frequency
Should not be computed for ordinal or interval data unless grouped.
Widely used in business
MEASURE OF DISPERSION
Measures of central tendency do not explain distribution of variables
4 measures of dispersion
1.Range
2.Variance and standard deviation
3.Coefficient of variation
4.Relative and absolute frequencies
DESCRIPTIVE ANALYSIS OF BIVARIATE DATA
There are three types of measure used.
1.Cross tabulation
2.Spearmans rank correlation coefficient
3.Pearsons linear correlation coefficient
Cross Tabulation
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Used in case of ordinal data
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Seasonal Rainfall Trend Analysis
This study aims to detect the trend in seasonal rainfall of four rainy months i.e. June, July, August and
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O M Unit 3 Forecasting
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Demandforecasting 1207335276942149-9
Demand forecasting involves determining customer demand for products and services in terms of what is needed, where, when, and in what quantities. It is a customer-focused activity that supports other planning functions and is the foundation of a company's logistics process. There are qualitative and quantitative methods for demand forecasting. Qualitative methods include surveys of buyer intentions, expert opinions, the Delphi method, market experimentation, and collective opinions. Quantitative methods include time series models, trend analysis, moving averages, exponential smoothing, and regression models.
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3...demand forecasting 1207335276942149-9
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Demand forecasting.
Demand forecasting involves estimating future demand for a product or service using both informal and quantitative methods. It is important for making pricing, production capacity, and market entry decisions. Methods include educated guesses, analyzing historical sales data, and using current test market data. Demand forecasting aims to minimize risks from an uncertain future by making reasonable assumptions about likely market conditions.
What Are Data Trends and Patterns, and How Do They Impact Business Decisions?
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Module 3 - Time Series.pptx
This document provides an overview of time series analysis and the Box-Jenkins methodology. Time series analysis attempts to model observations over time and identify patterns. The goals are to identify the structure of the time series and forecast future values. The Box-Jenkins methodology involves conditioning the data, selecting a model, estimating parameters, and assessing the model. Autocorrelation (ACF) and partial autocorrelation (PACF) plots are used to identify autoregressive (AR), moving average (MA), and autoregressive integrated moving average (ARIMA) models.
Final Time series analysis part 2. pptx
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This document discusses demand forecasting in supply chain management. Demand forecasts are critical for supply chain planning activities like production, inventory management, and workforce planning. The document outlines different forecasting time horizons and various qualitative and quantitative approaches to demand forecasting. Qualitative methods include jury of executive opinion, Delphi method, sales force composite, and market surveys. Quantitative time-series methods include naive approach, moving averages, exponential smoothing, and seasonal adjustments to account for trends and patterns in historical demand data. Accurate demand forecasting is essential for optimizing supply chain operations and responsiveness to customer needs.
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This document describes three approaches to time series forecasting: naive, averaging, and smoothing methods. Naive methods assume recent data best predicts the future. Averaging methods generate forecasts based on past observation averages. Smoothing methods produce forecasts by averaging past values with decreasing weights over time. Specific averaging and smoothing techniques discussed include simple and moving averages, exponential smoothing, and double moving averages to handle trended time series data.
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(๐๐๐ ๐๐๐) (๐๐๐ฌ๐ฌ๐จ๐ง ๐)-๐๐ซ๐๐ฅ๐ข๐ฆ๐ฌ
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ุตุญู ุงููุฑุงุกุงุช ุงูุนุดุฑ ุฃุนุฏ ุฃุญุฑู ุงูุฎูุงู ุณู
ูุฑ ุจุณูููู.pdf
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Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptx
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BรI TแบฌP Dแบ Y THรM TIแบพNG ANH LแปP 7 Cแบข NฤM FRIENDS PLUS SรCH CHรN TRแปI SรNG Tแบ O ...
BรI TแบฌP Dแบ Y THรM TIแบพNG ANH LแปP 7 Cแบข NฤM FRIENDS PLUS SรCH CHรN TRแปI SรNG Tแบ O ...
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Analysis of Time Series
- 2. TimeSeries - Meaning ๏ผA time series refers to the values of a variable, which are ordered chronologically, over a successive period of time. ๏ผIt is a sequence taken at successive equally spaced points in time. ๏ผIt discloses relationship between two variables, one being time. 2
- 4. Importance of Time Series Analysis Understanding past behavior 4 Predicting, Forecasting and Planning Evaluation of current programs Facilitates Comparison
- 5. Components of Time series ๏ฑThe statistical series are usually affected by a multiplicity of causes. ๏ฑSuch forces can be changes in population, tastes and habits of people, changes in income etc. ๏ฑThe effects of these forces on the time series are called the components of a time series Secular Trend Cyclic Variations Seasonal Variations Irregular Variations 5
- 6. SecularTrend ๏ฑTrend refers to long period changes. ๏ฑIt shows the definite and basic tendency of statistical data with the passage of time. ๏ฑThe trend can be Rising/Falling/Constant. ๏ฑThe concept of trend does not include short term oscilations. 6
- 9. Freehand Curve Method Semi Average Method Moving Average Method Method of Least Squares 9 Methodsof measuringtrend
- 11. 2.SemiAveragemethod 11 149 150 151 152 153 154 155 156 1990-1991 1994-1995 TREND LINE TREND LINE
- 12. 3.MovingAverageMethod 12 Year Value 1994 2 1995 5 1996 2 1997 2 1998 7 1999 6
- 13. โข Here, we are trying to establish a mathematical equation and using that equation to find the trend values. โข We are fitting a straight line to match the trend using a linear equation derived from the available data. Equation of straight line; y = a + bx 4.LinearTrend/Lineofbestfit. 13 Year (t) Value (y) Deviation from Mid year; t โ mid year (x) xy ๐2 ๐ = ๐ ๐ b = ๐๐ ๐ ๐
- 14. DrawingagraphโThings to remember! 14 0 1 2 3 4 5 6 2001 2002 2003 2004 Series 1 Series 2 Title for the graph Axis Description AxisDescription Line description Line description Intersection highlighting
- 15. Seasonal Variation ๏ฑThese are the variations which occur with some degree of regularity within a specific period of one year or shorter. 15
- 16. Simple Average Method Ratio to trend Method Ratio to Moving Average Method Method of link relatives 16 Methodsofmeasuring SeasonalVariations
- 17. โข A seasonal index is calculated by the average of each season. โข Here, a season means a month or a quarter or any other time period for which the fluctuations happen. ๏ฑ Step 1: Make a season wise arrangement of data ๏ฑ Step 2: Calculate the average for each season ๏ฑ Step 3: Calculate the average of averages ๏ฑ Step 4: Calculate seasonal index using the formula. 1.SimpleAverage Method. 17 Seasonal Index= ๐จ๐๐๐๐๐๐ ๐๐ ๐๐๐๐ ๐๐๐๐๐๐ ๐จ๐๐๐๐๐๐ ๐๐ ๐๐๐๐๐๐๐๐ ร 100
- 18. ๏ฑFirstly, we have to find the trend eliminated values using method of least squares ๏ฑThen method of simple average is applied on these trend eliminated values ๏ฑWe will then be getting the seasonal indices ๏ฑThis method is recommended only when there is absence of cyclical movements 2.Ratiototrendmethod 18 3.RatiotoMovingaveragemethod
- 19. 19 LorenzCurve
- 20. LorenzCurve It was developed by Max O. Lorenz in 1905 for representing inequality of the wealth distribution.. 20 It can also be used for studying the dispersion in series such as wages, production, population etc.. If there is no inequality in the distribution, lorenz curve will coincide with the line of equal distribution It is a graphic method of studying dispersion in a series
- 21. 21
- 22. THANKYOU Manu Antony +91 9567 320 002 cmanuantony@gmail.com www.contoso.com 22